Machine Learning on Mobile and Edge Devices with TensorFlow

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Machine Learning with Tensor Flow

The developments in the area of science and technology are constantly changing the business world. As a business owner and entrepreneur, one needs to gather a better understanding of the technology which goes way beyond the simple smartphone release. Today people are increasingly talking about artificial intelligence and machine learning. So what is this and how can it affect the business? What is the need to learn machine learning and how can it be done on mobile and edge devices with Tensor Flow? Let’s look ahead to find out how these tools can help marketers and businesses to grow and develop. 

Artificial intelligence and machine learning: A conceptual clarity 

The terms artificial intelligence and machine learning have been often used interchangeably. But in reality, these are not said to be the same. Artificial intelligence consists of a broader meaning. Artificial intelligence is the idea that machines and computers can complete the tasks that normally require human intelligence to be accomplished.

The theory of Machine learning is said to be a branch of the concept of AI. Machine Learning automates the model building for data analysis. The idea behind machine learning is believed that a computer has the potential to learn from the data which it analyzes with the help of identifying the patterns. In the end, this technology can make decisions without any human assistance.

Machine learning for the business: Establishing the connection 

In the present situation, businesses have been already using machine learning technology for assistance to improve their marketing programs and to increase their revenue by optimizing their customer experience. If a marketer or an entrepreneur does not have control over the marketing pulse, then the business could fall behind to its competitors. Even if a company is not ready to implement the technology of machine learning into their business, marketing strategy currently, they have to be prepared to do the same in close future.

Establishing the connection

Below mentioned are the best 5 ways through which machine learning is reshaping the marketing strategy of the modern-day businesses. 

  1. Better lead scoring accuracy

In present times, marketing professionals don’t possess the highest level of confidence in their lead scoring method. But once the marketer embraces the machine learning marketing into their system, their confidence levels should increase. This is because many of the factors go into doing such calculations, and machine learning can help the organization to make them.

  1. Better predictions of customer churn

There is a machine learning discovery model that can make predictions depending on the certain behaviors of the customers. This Machine learning helps to analyze the data on a much bigger scale and gives the marketers information to predict the churn as well as prevent it. 

  1. Beneficial dynamic pricing models

The dynamic pricing strategy lets the businesses to offer a variety of flexible prices for its products and services which they offer. This is a common model in the industries of hospitality, travel and tourism, and the entertainment sector. With the help of machine learning and AI, a dynamic pricing strategy can penetrate the retail industry as well. Generally, this pricing strategy helps to segment the prices based on the customer’s taste and preferences.

The Dynamic pricing concept is even related to the real-time pricing, that is when the value of the goods is based on certain market conditions. The sale of an airline ticket is a good example. Fixing the right prices is important for the success of the business. One can easily generate more and more profits by just focusing on their pricing strategy. Machine learning helps to use regression techniques to make market predictions. 

  1. Analysis of sentiment

The current businesses are not having a large number of face-to-face interactions with the consumers as they’re reaching out to the businesses via online modes. When the customer sends the company an email or a direct message, the business marketer needs to know how the customer is feeling to respond appropriately. The machine learning can do it for the company. The AI technology can easily analyze the text to determine whether the sentiment of the message sent to the business by the customer is either positive or negative. The information from the sentiment analysis is currently being used by marketers to better understand the company’s online reputation.

  1. Enhanced website experiments

The A/B testing is considered to be a great technique to improve the features of the company’s website, mobile app, as well as email marketing content. While the A/B tests will ultimately give the results to optimize a website, there are some downsides to this method. To get to the desired, the company might miss out on some opportunities. Machine learning can help a company in solving this problem by improving their bandit testing.

With the help of bandit testing, a solution with the greatest value gets prioritized. An algorithm of such a test will minimize the missed opportunities and thus make the experiments more beneficial.

Machine Learning and AI technology together can make up a great pair for marketing and its analysis for an enhanced level of service to the customers of the business. It is only when they are used together do they offer the desired results. They can be used separately but the result may vary depending on the situation and the task at hand. 

Read More: Why Every Business Should Use Machine Learning?

What is TensorFlow?

Install Tensorflow GPU
TensorFlow is said to be a production-ready, cross-platform framework that is used for deploying the application of ML on the mobile devices and the embedded systems. There is a growing need for people to become aware and knowledgeable about machine learning. The drivers for implementing the machine learning onto devices are threefold. They are as follows:

  • Reduced dependence on network connectivity
  • Lower latency
  • Privacy-preservation

With these drivers in place, a whole new group of products and services are made available on the different devices, ranging from video modification in real-time to the aspects of looking up the definitions of words by scanning the same on a phone. Presently more than 1000 applications are supported by TensorFlow which are run on almost three billion or more devices all over the world.

Other than mobile devices, TensorFlow can work on edge TPUs (Hardware Accelerators), Raspberry Pi (embedded Linux), and microcontrollers. This allows for machine learning “on the edge”. Having machine learning on the edge, the developers do not have to worry about privacy, security, bandwidth, latency, and complexity. But, there are some challenges, such as limited memory and battery life and limited computing power, on the microcontroller. Yet, TensorFlow can mitigate some of these mentioned challenges, and allows the developers to convert an already existing machine learning model for the use in TensorFlow and deploy the same on any device.

Read More: How To Install TensorFlow GPU 1.8 For Python 2.7 And Python 3.5 On Ubuntu 16.04?

TensorFlow is made up of four parts:

  • It provides several models that are out-of-the-box. These can be used by the developers or customized as per their needs.
  • It allows the conversion of the existing models which are found online or that are created by a particular organization.
  • It offers support for different languages as well as operating systems to provide support to the converted model and allow the deployment to any device.
  • It provides tools to enable the optimization of the different models to allow them to run faster and to take up less space on the devices. 

Master the path of machine learning 

To be an expert at machine learning, you need to have a strong foundation in the four learning areas namely math, ML theory, coding, and how to create your ML project right from the start to finish.

The best option available is TensorFlow’s curated curriculum which helps to improve the above mentioned four skills. One can easily choose their learning path by exploring the resource library of TensorFlow. Below mentioned are some details of the course offered.

Read More: Implementing Neural Networks with TensorFlow 

The four areas of machine learning education

While beginning the educational path, it is important to understand how to learn ML. TensorFlow has broken the learning process into primarily four areas, with each area offering a foundational piece to the ML puzzle. To help individuals on the learning path, there are several books, videos, and online courses that will help to up-level the individual abilities and prepare them to use ML for their projects. A brief description of the major areas of learning ML are explained below:

  • Coding skills: Creating ML models involves a lot more than simply knowing ML concepts—it needs coding to do handle parameter tuning, data management, and parsing results that are needed to test and to optimize the model.
  • Math and stats: Machine Learning is a math-heavy subject, so if one plans to modify the ML models or create new ones, a familiarity with the math concepts is important to the process.
  • ML theory: Knowing the basics of ML theory will provide individuals with a foundation to create on, and help to troubleshoot when anything goes wrong.
  • Building your projects: Having a hands-on experience with the ML application is the best method to put the knowledge to test, so one must not be afraid to dive early with a collab or a tutorial to get some practical exposure. 

Course offered for TensorFlow

Below mentioned are some of the options of the courses on TensorFlow that help in learning this technology. 

  1. Multi-part online courses

Undergoing a multi-part online course is an excellent way to have an idea about the basic concepts of ML. Several courses offer great visual explainers, along with the tools that are needed to start the application of machine learning directly at work, or maybe on a personal project. Some of the Multi-part online courses are as follows:

  1. Courses to build applications using TensorFlow

  1. Math concepts

To dwell deeper with the ML knowledge, the math resources can help to understand the important math concepts required for the higher level advancement.

Conclusion

Thus it can be seen that Machine learning is definitely here to stay, and it is not going anywhere else. Several organizations have already been using machine learning technology to change the way that they operate. If a business is aware of how to use machine learning to their advantage, then it can be an extremely valuable tool for the growth of the business. If you are an individual interested to grow your business with machine learning, then you can easily learn it from your mobile and edge devices with TensorFlow. With several options for customization available, one can learn machine learning the way they want and at the pace they desire. The availability of the courses on mobiles and edge devices with TensorFlow makes learning easy and it removes the restrictions of place and time. Working professionals or young engineers and entrepreneurs can take advantage of the time they have at hand and learn machine learning with convenience. 

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